{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "train-gan-painter.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reiinakano/neural-painters/blob/master/notebooks/train_gan_painter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "NVBXj5cqMeJ7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Optionally connect to Google Drive"
      ]
    },
    {
      "metadata": {
        "id": "y_yS_Zk7W_mh",
        "colab_type": "code",
        "outputId": "383b3f1e-1dee-4cb1-8079-ce32038ca489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "cell_type": "code",
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9P5f_uzAq_5g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Install MyPaint and dependencies"
      ]
    },
    {
      "metadata": {
        "id": "bAWyePtkn3av",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Install mypaint\n",
        "!apt-get update\n",
        "!apt-get install libjson-c-dev libgirepository1.0-dev libglib2.0-dev\n",
        "!apt-get install autotools-dev intltool gettext libtool\n",
        "!apt-get install swig python-setuptools gettext g++\n",
        "!apt-get install -y libgtk-3-dev python-gi-dev\n",
        "!apt-get install -y libpng-dev liblcms2-dev libjson-c-dev\n",
        "!apt-get install -y gir1.2-gtk-3.0 python-gi-cairo\n",
        "!apt-get install scons\n",
        "\n",
        "!wget https://github.com/mypaint/libmypaint/releases/download/v1.3.0/libmypaint-1.3.0.tar.xz\n",
        "!tar -xvf libmypaint-1.3.0.tar.xz\n",
        "!mv libmypaint-1.3.0 libmypaint\n",
        "\n",
        "!cd libmypaint && ./configure && make install\n",
        "\n",
        "!wget https://github.com/mypaint/mypaint/releases/download/v1.2.1/mypaint-1.2.1.tar.xz\n",
        "!tar -xvf mypaint-1.2.1.tar.xz\n",
        "!mv mypaint-1.2.1 mypaint\n",
        "!cd mypaint && scons && scons install\n",
        "\n",
        "!ldconfig\n",
        "\n",
        "!pip install ipdb tqdm pathlib cloudpickle  matplotlib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "61GvP5YaR4ao",
        "colab_type": "code",
        "outputId": "41fc29b9-054c-4a38-beaf-acf8209647a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install future-fstrings"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting future-fstrings\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/10/de62670513b7b2e7de32bbabb662cbc05ccee49fadf6f69725388df780e8/future_fstrings-1.0.0-py2.py3-none-any.whl\n",
            "Collecting tokenize-rt; python_version < \"3.6\" (from future-fstrings)\n",
            "  Downloading https://files.pythonhosted.org/packages/43/4b/c5df89ff5b38afffc04fb208c9b1fce30c1426788a368d7039b4cbcf524e/tokenize_rt-2.2.0-py2.py3-none-any.whl\n",
            "Installing collected packages: tokenize-rt, future-fstrings\n",
            "Successfully installed future-fstrings-1.0.0 tokenize-rt-2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1ueV31ATgbQp",
        "colab_type": "code",
        "outputId": "af72a2a4-48c5-4d2e-86ab-a05bb1cacccf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/reiinakano/SPIRAL-tensorflow.git\n",
        "!cd SPIRAL-tensorflow && git checkout reiinakano-patch-2  #reiinakano-patches\n",
        "!cd SPIRAL-tensorflow && git pull"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'SPIRAL-tensorflow'...\n",
            "remote: Enumerating objects: 8, done.\u001b[K\n",
            "remote: Counting objects: 100% (8/8), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 154 (delta 3), reused 0 (delta 0), pack-reused 146\u001b[K\n",
            "Receiving objects: 100% (154/154), 1.36 MiB | 1.51 MiB/s, done.\n",
            "Resolving deltas: 100% (75/75), done.\n",
            "Branch 'reiinakano-patch-2' set up to track remote branch 'reiinakano-patch-2' from 'origin'.\n",
            "Switched to a new branch 'reiinakano-patch-2'\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a2NYZTrugOeu",
        "colab_type": "code",
        "outputId": "82360cb1-7d7a-4ad7-8f48-e6d6bada53ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "cell_type": "code",
      "source": [
        "! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "! unzip ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-04-12 10:01:06--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 52.200.123.104, 34.204.22.7, 52.202.60.111, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|52.200.123.104|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 14977695 (14M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  14.28M  7.00MB/s    in 2.0s    \n",
            "\n",
            "2019-04-12 10:01:09 (7.00 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [14977695/14977695]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NfpE8439-xP5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download episodes. \n",
        "\n",
        "If you want to use/generate your own brushstrokes, go to https://github.com/reiinakano/diff-painter/blob/master/notebooks/generate_stroke_examples.ipynb or put your data under the data/ folder.\n",
        "\n",
        "Otherwise, you can download pre-calculated brushstrokes from Kaggle. You will need to set your API credentials via https://github.com/Kaggle/kaggle-api#api-credentials"
      ]
    },
    {
      "metadata": {
        "id": "tie8ynxlVmqH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rfdcEEY0VDlo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "\n",
        "# Make sure to upload your Kaggle key from somewhere. I saved mine in Drive\n",
        "#!cp \"/drive/My Drive/kaggle.json\" ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p_0SSM20WAIi",
        "colab_type": "code",
        "outputId": "188a18a7-2f62-41e4-8191-b32171034496",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "!kaggle datasets download reiinakano/mypaint_brushstrokes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading mypaint_brushstrokes.zip to /content\n",
            "100% 14.8G/14.8G [03:41<00:00, 86.8MB/s]\n",
            "100% 14.8G/14.8G [03:41<00:00, 71.9MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K6gtiCrAWRrv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!unzip mypaint_brushstrokes.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AbNUGvQVXyRF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mv strokes-dataset data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OWvFNFI1LpRV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# import"
      ]
    },
    {
      "metadata": {
        "id": "QYBRFuZqRqHL",
        "colab_type": "code",
        "outputId": "fe54fc92-e3a7-4dc9-c378-15bc4f1dcd92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "import json\n",
        "\n",
        "import tensorflow as tf\n",
        "import os\n",
        "\n",
        "import sys\n",
        "sys.path.append('mypaint')\n",
        "sys.path.append('SPIRAL-tensorflow')\n",
        "\n",
        "from lib import surface, tiledsurface, brush\n",
        "from envs.mypaint_utils import *\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow.contrib.layers as tcl\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "import moviepy.editor as mpy\n",
        "from moviepy.video.io.ffmpeg_writer import FFMPEG_VideoWriter\n",
        "\n",
        "import time\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n",
            "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n",
            "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1728512/45929032 bytes (3.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b5554176/45929032 bytes (12.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b9437184/45929032 bytes (20.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b13336576/45929032 bytes (29.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b17203200/45929032 bytes (37.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b20881408/45929032 bytes (45.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b24723456/45929032 bytes (53.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b28540928/45929032 bytes (62.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b32415744/45929032 bytes (70.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b36306944/45929032 bytes (79.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b40206336/45929032 bytes (87.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b44072960/45929032 bytes (96.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n",
            "  Done\n",
            "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n",
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f8HJ5tI7Lb_Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# paint environment"
      ]
    },
    {
      "metadata": {
        "id": "BAaXkkWhRzWt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class args:\n",
        "  jump=True\n",
        "  curve=True\n",
        "  screen_size=64\n",
        "  location_size=32\n",
        "  color_channel=3\n",
        "  brush_path='SPIRAL-tensorflow/assets/brushes/dry_brush.myb'\n",
        "  train=True\n",
        "  data_dir=Path('data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2z3CXRd1TRma",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class PaintMode:\n",
        "  STROKES_ONLY = 0\n",
        "  JUMP_STROKES = 1\n",
        "  CONNECTED_STROKES = 2\n",
        "\n",
        "class ColorEnv():\n",
        "    head = 0.25\n",
        "    tail = 0.75\n",
        "    \n",
        "    # all 0 to 1\n",
        "    actions_to_idx = {\n",
        "        'pressure': 0,\n",
        "        'size': 1,\n",
        "        'control_x': 2,\n",
        "        'control_y': 3,\n",
        "        'end_x': 4,\n",
        "        'end_y': 5,\n",
        "        'color_r': 6,\n",
        "        'color_g': 7,\n",
        "        'color_b': 8,\n",
        "        'start_x': 9,\n",
        "        'start_y': 10,\n",
        "        'entry_pressure': 11,\n",
        "    }\n",
        "\n",
        "    def __init__(self, args, paint_mode=PaintMode.JUMP_STROKES):\n",
        "        self.args = args\n",
        "        self.paint_mode = paint_mode\n",
        "\n",
        "        # screen\n",
        "        self.screen_size = args.screen_size\n",
        "        self.height, self.width = self.screen_size, self.screen_size\n",
        "        self.observation_shape = [\n",
        "                self.height, self.width, args.color_channel]\n",
        "\n",
        "        # location\n",
        "        self.location_size = args.location_size\n",
        "        self.location_shape = [self.location_size, self.location_size]\n",
        "        \n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "    \n",
        "    @staticmethod\n",
        "    def pretty_print_action(ac):\n",
        "        for k, v in ColorEnv.actions_to_idx.items():\n",
        "            print(k, ac[v])\n",
        "    \n",
        "    def random_action(self):\n",
        "        return np.random.uniform(size=[len(self.actions_to_idx)])\n",
        "      \n",
        "    def reset(self):\n",
        "        self.intermediate_images = []\n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "\n",
        "        self.s = tiledsurface.Surface()\n",
        "        self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "        with open(self.args.brush_path) as fp:\n",
        "            self.bi = brush.BrushInfo(fp.read())\n",
        "        self.b = brush.Brush(self.bi)\n",
        "\n",
        "    def draw(self, ac, s=None, dtime=1):\n",
        "        # Just added this\n",
        "        if self.paint_mode == PaintMode.STROKES_ONLY:\n",
        "          self.s.clear()\n",
        "          self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "          self.s.end_atomic()\n",
        "          self.s.begin_atomic()\n",
        "        \n",
        "        if s is None:\n",
        "            s = self.s\n",
        "\n",
        "        s_x, s_y = ac[self.actions_to_idx['start_x']]*64, ac[self.actions_to_idx['start_y']]*64  \n",
        "        e_x, e_y = ac[self.actions_to_idx['end_x']]*64, ac[self.actions_to_idx['end_y']]*64\n",
        "        c_x, c_y = ac[self.actions_to_idx['control_x']]*64, ac[self.actions_to_idx['control_y']]*64\n",
        "        color = (\n",
        "            ac[self.actions_to_idx['color_r']],\n",
        "            ac[self.actions_to_idx['color_g']],\n",
        "            ac[self.actions_to_idx['color_b']],\n",
        "        )\n",
        "        pressure = ac[self.actions_to_idx['pressure']]*0.8\n",
        "        entry_pressure = ac[self.actions_to_idx['entry_pressure']]*0.8\n",
        "        size = ac[self.actions_to_idx['size']] * 2.\n",
        "        \n",
        "        if self.paint_mode == PaintMode.CONNECTED_STROKES:\n",
        "            if self.prev_x is not None:\n",
        "                s_x, s_y, entry_pressure = self.prev_x, self.prev_y, self.prev_pressure\n",
        "            self.prev_x, self.prev_y, self.prev_pressure = e_x, e_y, pressure\n",
        "\n",
        "        self.b.brushinfo.set_color_rgb(color)\n",
        "        \n",
        "        self.b.brushinfo.set_base_value('radius_logarithmic', size)\n",
        "\n",
        "        # Move brush to starting point without leaving it on the canvas.\n",
        "        self._stroke_to(s_x, s_y, 0)\n",
        "\n",
        "        self._draw(s_x, s_y, e_x, e_y, c_x, c_y, entry_pressure, pressure, size, color, dtime)\n",
        "\n",
        "    def _draw(self, s_x, s_y, e_x, e_y, c_x, c_y,\n",
        "              entry_pressure, pressure, size, color, dtime):\n",
        "\n",
        "        # if straight line or jump\n",
        "        if pressure == 0:\n",
        "            self.b.stroke_to(\n",
        "                    self.s.backend, e_x, e_y, pressure, 0, 0, dtime)\n",
        "        else:\n",
        "            self.curve(c_x, c_y, s_x, s_y, e_x, e_y, entry_pressure, pressure)\n",
        "            \n",
        "        # Relieve brush pressure for next jump\n",
        "        self._stroke_to(e_x, e_y, 0)\n",
        "\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "    # sx, sy = starting point\n",
        "    # ex, ey = end point\n",
        "    # kx, ky = curve point from last line\n",
        "    # lx, ly = last point from InteractionMode update\n",
        "    def curve(self, cx, cy, sx, sy, ex, ey, entry_pressure, pressure):\n",
        "        #entry_p, midpoint_p, junk, prange2, head, tail\n",
        "        entry_p, midpoint_p, prange1, prange2, h, t = \\\n",
        "                self._line_settings(entry_pressure, pressure)\n",
        "\n",
        "        points_in_curve = 100\n",
        "        mx, my = midpoint(sx, sy, ex, ey)\n",
        "        length, nx, ny = length_and_normal(mx, my, cx, cy)\n",
        "        cx, cy = multiply_add(mx, my, nx, ny, length*2)\n",
        "        x1, y1 = difference(sx, sy, cx, cy)\n",
        "        x2, y2 = difference(cx, cy, ex, ey)\n",
        "        head = points_in_curve * h\n",
        "        head_range = int(head)+1\n",
        "        tail = points_in_curve * t\n",
        "        tail_range = int(tail)+1\n",
        "        tail_length = points_in_curve - tail\n",
        "\n",
        "        # Beginning\n",
        "        px, py = point_on_curve_1(1, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "        length, nx, ny = length_and_normal(sx, sy, px, py)\n",
        "        bx, by = multiply_add(sx, sy, nx, ny, 0.25)\n",
        "        self._stroke_to(bx, by, entry_p)\n",
        "        pressure = abs(1/head * prange1 + entry_p)\n",
        "        self._stroke_to(px, py, pressure)\n",
        "\n",
        "        for i in xrange(2, head_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs(i/head * prange1 + entry_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        # Middle\n",
        "        for i in xrange(head_range, tail_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            self._stroke_to(px, py, midpoint_p)\n",
        "\n",
        "        # End\n",
        "        for i in xrange(tail_range, points_in_curve+1):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs((i-tail)/tail_length * prange2 + midpoint_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        return pressure\n",
        "\n",
        "    def _stroke_to(self, x, y, pressure, duration=0.1):\n",
        "        self.b.stroke_to(\n",
        "                self.s.backend,\n",
        "                x, y,\n",
        "                pressure,\n",
        "                0.0, 0.0,\n",
        "                duration)\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "        self.intermediate_images.append(self.image)\n",
        "\n",
        "    def save_image(self, path=\"test.png\"):\n",
        "        Image.fromarray(self.image.astype(np.uint8).squeeze()).save(path)\n",
        "        #self.s.save_as_png(path, alpha=False)\n",
        "\n",
        "    @property\n",
        "    def image(self):\n",
        "        rect = [0, 0, self.height, self.width]\n",
        "        scanline_strips = \\\n",
        "                surface.scanline_strips_iter(self.s, rect)\n",
        "        return next(scanline_strips)\n",
        "\n",
        "    def _line_settings(self, entry_pressure, pressure):\n",
        "        p1 = entry_pressure\n",
        "        p2 = (entry_pressure + pressure) / 2\n",
        "        p3 = pressure\n",
        "        if self.head == 0.0001:\n",
        "            p1 = p2\n",
        "        prange1 = p2 - p1\n",
        "        prange2 = p3 - p2\n",
        "        return p1, p2, prange1, prange2, self.head, self.tail\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nB3Xr8N5A0NQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Sanity check data"
      ]
    },
    {
      "metadata": {
        "id": "nLLVVhBGjdX5",
        "colab_type": "code",
        "outputId": "02b6d12b-e153-4a21-aba0-6c8f1effa582",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "loaded = np.load('data/episodes_0.npz')\n",
        "\n",
        "w=args.screen_size\n",
        "h=args.screen_size\n",
        "fig=plt.figure(figsize=(30, 10))\n",
        "cols = 10\n",
        "\n",
        "smaller_arr = loaded['strokes'][:cols]\n",
        "\n",
        "for col in range(cols):\n",
        "    img = smaller_arr[col][:, :, :3]\n",
        "    #print(img.shape)\n",
        "    fig.add_subplot(1, 20, col+1)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l8VWmqpPcN3UfJ8dPMFOfGb1XYGcRRarcZq/NQKfMtaapJhViFZaht4JzPgZmncWOnDHf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\nhmEYhmEYO8B/ADSNuV0X/UVBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 2160x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4OQ0qyTrYuv1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for ac in loaded['actions'][:cols]:\n",
        "  print('___')\n",
        "  ColorEnv.pretty_print_action(ac)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-tVObgb7LEow",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gm-BNgebLwMY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GAN Neural Painter"
      ]
    },
    {
      "metadata": {
        "id": "JVwCHbiOL8la",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def leaky_relu(x, alpha=0.2):\n",
        "    return tf.maximum(tf.minimum(0.0, alpha * x), x)\n",
        "\n",
        "def leaky_relu_batch_norm(x, alpha=0.2):\n",
        "    return leaky_relu(tf.contrib.layers.batch_norm(x, updates_collections=None), alpha)\n",
        "\n",
        "def relu_batch_norm(x):\n",
        "    return tf.nn.relu(tf.contrib.layers.batch_norm(x, updates_collections=None))\n",
        "  \n",
        "class DiscriminatorConditionalWM(object):\n",
        "    def __init__(self, divisor=1):\n",
        "        \"\"\"\n",
        "        make the network smaller by divisor times\n",
        "        \"\"\"\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.name = 'lsun/dcgan/d_net'\n",
        "        self.divisor=divisor\n",
        "        \n",
        "    def __call__(self, x, conditions, reuse=True):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(x)[0]\n",
        "            x = tf.reshape(x, [bs, 64, 64, 3])\n",
        "            conditions = tf.reshape(conditions, [bs, 12])\n",
        "            conditions = tf.contrib.layers.fully_connected(conditions, 64/self.divisor)\n",
        "            conditions = tf.reshape(conditions, [bs, 1, 1, 64/self.divisor])\n",
        "            conv1 = tcl.conv2d(\n",
        "                x, 64/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu\n",
        "            )\n",
        "            conv1 = conv1 + conditions\n",
        "            conv2 = tcl.conv2d(\n",
        "                conv1, 128/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv3 = tcl.conv2d(\n",
        "                conv2, 256/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.conv2d(\n",
        "                conv3, 512/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.flatten(conv4)\n",
        "            fc = tcl.fully_connected(conv4, 1, activation_fn=tf.identity)\n",
        "            return fc\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "\n",
        "class GeneratorConditional(object):\n",
        "    def __init__(self, divisor=1, add_noise=False):\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.divisor=divisor\n",
        "        self.name = 'lsun/dcgan/g_net'\n",
        "        self.add_noise = add_noise\n",
        "\n",
        "    def __call__(self, conditions, is_training):\n",
        "        with tf.contrib.framework.arg_scope([tcl.batch_norm], \n",
        "                                            is_training=is_training):\n",
        "          with tf.variable_scope(self.name) as vs:\n",
        "              bs = tf.shape(conditions)[0]\n",
        "              if self.add_noise:\n",
        "                conditions = tf.concat([conditions, tf.random.uniform([bs, 10])], axis=1)\n",
        "              fc = tcl.fully_connected(conditions, 4 * 4 * 1024/self.divisor, activation_fn=tf.identity)\n",
        "              conv1 = tf.reshape(fc, tf.stack([bs, 4, 4, 1024/self.divisor]))\n",
        "              conv1 = relu_batch_norm(conv1)\n",
        "              conv2 = tcl.conv2d_transpose(\n",
        "                  conv1, 512/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv3 = tcl.conv2d_transpose(\n",
        "                  conv2, 256/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv4 = tcl.conv2d_transpose(\n",
        "                  conv3, 128/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv5 = tcl.conv2d_transpose(\n",
        "                  conv4, 3, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=tf.sigmoid)\n",
        "              return conv5\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WhkS4KSjR3jg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ConvGAN(object):\n",
        "  def __init__(self, learning_rate=0.0001, is_training=True, reuse=False, gpu_mode=True, data_glob='data/episodes_*.npz', divisor=1, add_noise=False, graph=None):\n",
        "    self.learning_rate = learning_rate\n",
        "    self.is_training = is_training\n",
        "    self.reuse = reuse\n",
        "    self.g_net = GeneratorConditional(divisor=divisor, add_noise=add_noise)\n",
        "    self.d_net = DiscriminatorConditionalWM(divisor=divisor)\n",
        "    \n",
        "    if self.is_training:\n",
        "      self.episode_files = tf.gfile.Glob(data_glob)\n",
        "      np.random.shuffle(self.episode_files)\n",
        "      self.ep_file_ctr = 0\n",
        "      self.current_file_idx = 0\n",
        "      loaded = np.load(self.episode_files[0])\n",
        "      self.loaded_strokes = loaded['strokes']\n",
        "      self.loaded_actions = loaded['actions']\n",
        "      self.len_loaded = len(self.loaded_strokes)\n",
        "    \n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('conv_gan using cpu.')\n",
        "        self._build_training_or_generator_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('conv_gan using gpu.')\n",
        "      self._build_training_or_generator_graph(graph)\n",
        "    self._init_session()\n",
        "   \n",
        "  def _build_training_or_generator_graph(self, graph):\n",
        "    if self.is_training:\n",
        "      self._build_graph(graph)\n",
        "    else:\n",
        "      self._build_generator_graph(graph)\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    with self.g.as_default(), tf.variable_scope('conv_gan', reuse=self.reuse):\n",
        "\n",
        "      self.x = tf.placeholder(tf.float32, shape=[None, 64, 64, 3])\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      \n",
        "      self.y = self.g_net(self.actions, is_training=self.is_training)\n",
        "      \n",
        "      real_score = tf.reduce_mean(self.d_net(self.x, self.actions, reuse=False))\n",
        "      generated_score = tf.reduce_mean(self.d_net(self.y, self.actions))\n",
        "      \n",
        "      real_score_summ = tf.summary.scalar('real_score', real_score)\n",
        "      g_loss_summ = tf.summary.scalar('generated_score/g_loss', generated_score)\n",
        "      \n",
        "      reconstruction_loss = tf.reduce_mean(tf.square(self.y-self.x))\n",
        "      recon_loss_summ = tf.summary.scalar('recon_loss', reconstruction_loss)\n",
        "      \n",
        "      # actual reconstruction loss used for training\n",
        "      self.uneven_multiplier = tf.placeholder_with_default(1.0, [])\n",
        "      u_m_summ = tf.summary.scalar('uneven_multiplier', self.uneven_multiplier)\n",
        "      mask = tf.reduce_mean(\n",
        "        self.x,\n",
        "        reduction_indices = [3]\n",
        "      )\n",
        "      stroke_whitespace = tf.equal(mask, 1.0)\n",
        "      mask = tf.where(stroke_whitespace, tf.ones(tf.shape(mask)), self.uneven_multiplier*tf.ones(tf.shape(mask)))\n",
        "      mask = tf.reshape(mask, [-1, 64, 64, 1])\n",
        "      mask = tf.tile(mask, [1, 1, 1, 3])\n",
        "      tf.summary.image('mask', mask, max_outputs=3)\n",
        "      print(mask.get_shape())\n",
        "      self.r_loss = 10*tf.reduce_mean((tf.square(self.x - self.y))*mask)\n",
        "      actual_recon_loss_summ = tf.summary.scalar('recon_loss_actual', self.r_loss)\n",
        "      # /actual reconstruction loss used for training\n",
        "      \n",
        "      self.g_loss = generated_score + self.r_loss\n",
        "      self.d_loss = real_score - generated_score\n",
        "      \n",
        "      epsilon = tf.random_uniform([], 0.0, 1.0)\n",
        "      x_hat = epsilon * self.x + (1 - epsilon) * self.y\n",
        "      d_hat = self.d_net(x_hat, self.actions)\n",
        "      \n",
        "      ddx = tf.gradients(d_hat, x_hat)[0]\n",
        "      print(ddx.get_shape().as_list())\n",
        "\n",
        "      ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=(1, 2, 3)))\n",
        "      ddx = tf.reduce_mean(tf.square(ddx - 1.0) * 10.0)\n",
        "      \n",
        "      self.d_loss = self.d_loss + ddx\n",
        "      \n",
        "      gradient_penalty_summ = tf.summary.scalar('gradient_penalty', ddx)\n",
        "      d_loss_summ = tf.summary.scalar('actual_loss', self.d_loss)\n",
        "      \n",
        "      # train ops\n",
        "      if self.is_training:\n",
        "        self.global_step = tf.Variable(0, name='global_step', trainable=False)\n",
        "        self.inc_global_step = tf.assign(self.global_step, self.global_step+1)\n",
        "\n",
        "        self.d_adam, self.g_adam = None, None\n",
        "        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "            self.d_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "                .minimize(self.d_loss, var_list=self.d_net.vars)\n",
        "            self.g_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "                .minimize(self.g_loss, var_list=self.g_net.vars)\n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "      \n",
        "      #summary ops\n",
        "      tf.summary.image('inp_img', self.x, max_outputs=3)\n",
        "      tf.summary.image('output_img', self.y, max_outputs=3)\n",
        "      self.summary_op = tf.summary.merge_all()\n",
        "      \n",
        "  def _build_generator_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "      \n",
        "    with self.g.as_default(), tf.variable_scope('conv_gan', reuse=self.reuse):\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      self.y = self.g_net(self.actions, is_training=self.is_training)\n",
        "      self.init = tf.global_variables_initializer()\n",
        "  \n",
        "  def generate_stroke_graph(self, actions):\n",
        "    with tf.variable_scope('conv_gan', reuse=True):\n",
        "      with self.g.as_default():\n",
        "        return self.g_net(actions, is_training=False)\n",
        "      \n",
        "  def get_random_batch(self, batch_size):\n",
        "    while self.len_loaded - self.current_file_idx < batch_size:\n",
        "      self.ep_file_ctr = (self.ep_file_ctr + 1) % len(self.episode_files)\n",
        "      self.current_file_idx = 0\n",
        "      print('loading new file', self.episode_files[self.ep_file_ctr])\n",
        "      loaded = np.load(self.episode_files[self.ep_file_ctr])\n",
        "      self.loaded_strokes = loaded['strokes']\n",
        "      self.loaded_actions = loaded['actions']\n",
        "      self.len_loaded = len(self.loaded_strokes)\n",
        "      rand_idx = np.random.permutation(self.len_loaded)\n",
        "      self.loaded_strokes = self.loaded_strokes[rand_idx]\n",
        "      self.loaded_actions = self.loaded_actions[rand_idx]\n",
        "    \n",
        "    strokes = self.loaded_strokes[self.current_file_idx:self.current_file_idx+batch_size]\n",
        "    strokes = strokes.astype(np.float)/255.0\n",
        "    actions = self.loaded_actions[self.current_file_idx:self.current_file_idx+batch_size]\n",
        "    \n",
        "    self.current_file_idx += batch_size\n",
        "    \n",
        "    return strokes, actions\n",
        "      \n",
        "  def train(self, batch_size=64, num_batches=1000000):\n",
        "    train_writer = tf.summary.FileWriter('logdir', self.g)\n",
        "    start_time = time.time()\n",
        "    uneven_multiplier = 10.\n",
        "    for t in range(num_batches):\n",
        "        d_iters = 5\n",
        "\n",
        "        for _ in range(0, d_iters):\n",
        "            strokes, actions = self.get_random_batch(batch_size)\n",
        "            _, summ = self.sess.run((self.d_adam, self.summary_op), \n",
        "                                    feed_dict={self.x: strokes, self.actions: actions})\n",
        "\n",
        "        strokes, actions = self.get_random_batch(batch_size)\n",
        "        _, summ, _, step = self.sess.run((self.g_adam, self.summary_op, self.inc_global_step, self.global_step), \n",
        "                                      feed_dict={self.x: strokes, \n",
        "                                                 self.actions: actions, \n",
        "                                                 self.uneven_multiplier: uneven_multiplier})\n",
        "        train_writer.add_summary(summ, step)\n",
        "        \n",
        "        if step > 295044:\n",
        "          uneven_multiplier = 1.\n",
        "\n",
        "        if step % 20 == 0:\n",
        "            strokes, actions = self.get_random_batch(batch_size)\n",
        "            d_loss = self.sess.run(\n",
        "                self.d_loss, feed_dict={self.x: strokes, self.actions: actions}\n",
        "            )\n",
        "            g_loss = self.sess.run(\n",
        "                self.g_loss, feed_dict={self.x: strokes, self.actions: actions}\n",
        "            )\n",
        "            print('Iter [%8d] Time [%5.4f] d_loss [%.4f] g_loss [%.4f]' %\n",
        "                    (step, time.time() - start_time, d_loss, g_loss))\n",
        "            \n",
        "        if step % 2000 == 0:\n",
        "            self.save_model()\n",
        "\n",
        "  def _init_session(self):\n",
        "    \"\"\"Launch TensorFlow session and initialize variables\"\"\"\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    \"\"\" Close TensorFlow session \"\"\"\n",
        "    self.sess.close()\n",
        "  def save_model(self, model_save_path='tf_gan3'):\n",
        "    sess = self.sess\n",
        "    step = sess.run(self.global_step)\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'gan')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, step) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='tf_gan3', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lTw5sevmA_Sb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training the GAN Neural painter"
      ]
    },
    {
      "metadata": {
        "id": "AS1ud5C1ZYQL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Hyperparameters for ConvVAE\n",
        "batch_size=100\n",
        "learning_rate=0.0001\n",
        "\n",
        "# Parameters for training\n",
        "NUM_EPOCH = 10\n",
        "DATA_DIR = \"record\"\n",
        "\n",
        "model_save_path = \"tf_gan3\"\n",
        "if not os.path.exists(model_save_path):\n",
        "  os.makedirs(model_save_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "caVhW8HYYwpE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def reset_graph():\n",
        "  if 'sess' in globals() and sess:\n",
        "    sess.close()\n",
        "  tf.reset_default_graph()\n",
        "\n",
        "reset_graph()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5Fh546j_Z9KC",
        "colab_type": "code",
        "outputId": "529123b3-4ac5-4c0c-ff66-a9042a042663",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "cell_type": "code",
      "source": [
        "gan = ConvGAN(learning_rate=learning_rate,\n",
        "              is_training=True,\n",
        "              reuse=False,\n",
        "              gpu_mode=True,\n",
        "              divisor=4,\n",
        "              add_noise=False)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:conv_gan using gpu.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "(?, 64, 64, 3)\n",
            "[None, 64, 64, 3]\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bbnQ2kN-Bd9l",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Running tensorboard"
      ]
    },
    {
      "metadata": {
        "id": "yV4rMj60r74b",
        "colab_type": "code",
        "outputId": "8e7ee458-cc15-4c78-a706-aaf451344045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# Delete old logdir\n",
        "#!rm -r logdir\n",
        "\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format('logdir')\n",
        ")\n",
        "\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "\n",
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://9943c449.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oBVSMtZSBhEU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Actual training"
      ]
    },
    {
      "metadata": {
        "id": "LIVXqTShaG4e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Run this to start from a checkpoint\n",
        "# gan.load_checkpoint('tf_gan3')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YaVPx9F5BnV0",
        "colab_type": "code",
        "outputId": "4de83d46-cfd6-4116-ad8a-418ab33ea5c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 14085
        }
      },
      "cell_type": "code",
      "source": [
        "gan.train()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iter [   45180] Time [16779.1555] d_loss [-0.3270] g_loss [21.1908]\n",
            "('loading new file', 'data/episodes_21.npz')\n",
            "Iter [   45200] Time [16792.5670] d_loss [-0.2331] g_loss [22.9276]\n",
            "Iter [   45220] Time [16799.3478] d_loss [-0.4615] g_loss [20.1239]\n",
            "Iter [   45240] Time [16806.1286] d_loss [-0.1133] g_loss [25.6514]\n",
            "Iter [   45260] Time [16812.8861] d_loss [-0.4056] g_loss [22.6948]\n",
            "Iter [   45280] Time [16819.6849] d_loss [-0.3526] g_loss [23.2790]\n",
            "Iter [   45300] Time [16826.4535] d_loss [-0.7022] g_loss [24.4875]\n",
            "Iter [   45320] Time [16833.2255] d_loss [-0.4282] g_loss [20.7543]\n",
            "Iter [   45340] Time [16840.0225] d_loss [-0.2764] g_loss [21.9948]\n",
            "Iter [   45360] Time [16846.7953] d_loss [0.0425] g_loss [17.6599]\n",
            "Iter [   45380] Time [16853.5734] d_loss [-0.2378] g_loss [18.9157]\n",
            "Iter [   45400] Time [16860.3679] d_loss [-0.3009] g_loss [24.2596]\n",
            "Iter [   45420] Time [16867.1855] d_loss [-0.4271] g_loss [22.0943]\n",
            "Iter [   45440] Time [16873.9668] d_loss [-0.1427] g_loss [20.5695]\n",
            "('loading new file', 'data/episodes_1.npz')\n",
            "Iter [   45460] Time [16887.3874] d_loss [-0.0370] g_loss [24.8681]\n",
            "Iter [   45480] Time [16894.2033] d_loss [-0.4430] g_loss [22.3238]\n",
            "Iter [   45500] Time [16901.0247] d_loss [-0.4751] g_loss [22.7243]\n",
            "Iter [   45520] Time [16907.8201] d_loss [-0.1719] g_loss [22.6164]\n",
            "Iter [   45540] Time [16914.5968] d_loss [-0.7564] g_loss [22.3888]\n",
            "Iter [   45560] Time [16921.4078] d_loss [-0.1938] g_loss [18.0474]\n",
            "Iter [   45580] Time [16928.2443] d_loss [-0.2752] g_loss [20.5593]\n",
            "Iter [   45600] Time [16935.0557] d_loss [-0.4015] g_loss [22.3318]\n",
            "Iter [   45620] Time [16941.8426] d_loss [-0.3999] g_loss [24.3690]\n",
            "Iter [   45640] Time [16948.6025] d_loss [-0.5352] g_loss [19.5178]\n",
            "Iter [   45660] Time [16955.3163] d_loss [-0.2145] g_loss [24.9262]\n",
            "Iter [   45680] Time [16962.0861] d_loss [-0.7112] g_loss [21.2045]\n",
            "('loading new file', 'data/episodes_65.npz')\n",
            "Iter [   45700] Time [16975.4971] d_loss [-0.3982] g_loss [18.3307]\n",
            "Iter [   45720] Time [16982.3028] d_loss [0.0533] g_loss [20.8219]\n",
            "Iter [   45740] Time [16989.1189] d_loss [-0.2885] g_loss [23.1061]\n",
            "Iter [   45760] Time [16995.8882] d_loss [-0.1026] g_loss [18.2878]\n",
            "Iter [   45780] Time [17002.6794] d_loss [-0.3576] g_loss [18.9175]\n",
            "Iter [   45800] Time [17009.4599] d_loss [-0.4047] g_loss [22.3411]\n",
            "Iter [   45820] Time [17016.3596] d_loss [-0.0349] g_loss [19.7522]\n",
            "Iter [   45840] Time [17023.1287] d_loss [-0.1124] g_loss [21.3340]\n",
            "Iter [   45860] Time [17029.9147] d_loss [-0.3154] g_loss [18.8457]\n",
            "Iter [   45880] Time [17036.6922] d_loss [-0.3610] g_loss [17.4883]\n",
            "Iter [   45900] Time [17043.4995] d_loss [-0.0948] g_loss [22.9268]\n",
            "Iter [   45920] Time [17050.3040] d_loss [-0.2901] g_loss [19.6523]\n",
            "Iter [   45940] Time [17057.0868] d_loss [-0.5302] g_loss [22.5517]\n",
            "('loading new file', 'data/episodes_77.npz')\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
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            "('loading new file', 'data/episodes_20.npz')\n",
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            "('loading new file', 'data/episodes_16.npz')\n",
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            "('loading new file', 'data/episodes_56.npz')\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
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            "('loading new file', 'data/episodes_32.npz')\n",
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            "('loading new file', 'data/episodes_71.npz')\n",
            "Iter [   49840] Time [18496.9043] d_loss [-0.2141] g_loss [23.7796]\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
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            "('loading new file', 'data/episodes_63.npz')\n",
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            "('loading new file', 'data/episodes_2.npz')\n",
            "Iter [   54480] Time [20205.4112] d_loss [-0.2591] g_loss [23.8505]\n",
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            "('loading new file', 'data/episodes_26.npz')\n",
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            "('loading new file', 'data/episodes_0.npz')\n",
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            "('loading new file', 'data/episodes_50.npz')\n",
            "Iter [   55780] Time [20683.3741] d_loss [-0.4025] g_loss [23.7126]\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
            "Iter [   56020] Time [20766.5952] d_loss [-0.1200] g_loss [21.1834]\n",
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            "('loading new file', 'data/episodes_18.npz')\n",
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            "('loading new file', 'data/episodes_3.npz')\n",
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            "Iter [   57580] Time [21347.3674] d_loss [-0.1976] g_loss [21.2546]\n",
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            "('loading new file', 'data/episodes_53.npz')\n",
            "Iter [   57840] Time [21443.1994] d_loss [-0.2907] g_loss [20.9808]\n",
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            "INFO:tensorflow:saving model tf_gan3/gan.\n",
            "Iter [   58020] Time [21505.9593] d_loss [-0.2606] g_loss [25.4329]\n",
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            "('loading new file', 'data/episodes_74.npz')\n",
            "Iter [   58100] Time [21540.1760] d_loss [-0.4502] g_loss [20.9782]\n",
            "Iter [   58120] Time [21547.0340] d_loss [0.0319] g_loss [23.7544]\n",
            "Iter [   58140] Time [21553.8735] d_loss [-0.2020] g_loss [24.4505]\n",
            "Iter [   58160] Time [21560.7317] d_loss [-0.2929] g_loss [25.7213]\n",
            "Iter [   58180] Time [21567.5748] d_loss [-0.6210] g_loss [24.9294]\n",
            "Iter [   58200] Time [21574.4785] d_loss [-0.3683] g_loss [23.9045]\n",
            "Iter [   58220] Time [21581.3468] d_loss [-0.0576] g_loss [18.6211]\n",
            "Iter [   58240] Time [21588.1800] d_loss [-0.3344] g_loss [17.2760]\n",
            "Iter [   58260] Time [21595.0608] d_loss [0.2202] g_loss [21.9218]\n",
            "Iter [   58280] Time [21601.9118] d_loss [-0.3600] g_loss [19.2115]\n",
            "Iter [   58300] Time [21608.7586] d_loss [-0.1911] g_loss [22.1369]\n",
            "Iter [   58320] Time [21615.5220] d_loss [-0.7018] g_loss [20.6271]\n",
            "Iter [   58340] Time [21622.3545] d_loss [-0.2807] g_loss [22.1252]\n",
            "('loading new file', 'data/episodes_22.npz')\n",
            "Iter [   58360] Time [21635.9159] d_loss [-0.1479] g_loss [19.4881]\n",
            "Iter [   58380] Time [21642.7585] d_loss [-0.3330] g_loss [25.1617]\n",
            "Iter [   58400] Time [21649.5635] d_loss [-0.1316] g_loss [23.8391]\n",
            "Iter [   58420] Time [21656.4108] d_loss [-0.4188] g_loss [21.2245]\n",
            "Iter [   58440] Time [21663.2318] d_loss [0.2847] g_loss [18.9493]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-8827ae40166e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-17-25e3f31a9cb4>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, batch_size, num_batches)\u001b[0m\n\u001b[1;32m    159\u001b[0m                                       feed_dict={self.x: strokes, \n\u001b[1;32m    160\u001b[0m                                                  \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m                                                  self.uneven_multiplier: uneven_multiplier})\n\u001b[0m\u001b[1;32m    162\u001b[0m         \u001b[0mtrain_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msumm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    927\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 929\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    930\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1150\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1152\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1153\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1328\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1329\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1332\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1335\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1317\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1319\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1405\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "pSn_NZKpFeDT",
        "colab_type": "code",
        "outputId": "8ac716ce-9962-40e0-ead7-ea66b982d916",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "gan.save_model()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:saving model tf_gan3/gan.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FtqK9mooRBz3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Test your trained model"
      ]
    },
    {
      "metadata": {
        "id": "m7sZrojtli7_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "loaded = np.load('data/episodes_0.npz')\n",
        "images = loaded['strokes']\n",
        "images = np.reshape(images[-100:], [-1, 64, 64, 3])\n",
        "actions = loaded['actions'][-100:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XmmzAmIkb3Ft",
        "colab_type": "code",
        "outputId": "86e2650d-096c-468b-9eb7-654febfe1bfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "test_gan = ConvGAN(learning_rate=learning_rate,\n",
        "              is_training=False,\n",
        "              reuse=False,\n",
        "              gpu_mode=False,\n",
        "              divisor=4,\n",
        "              add_noise=False)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:conv_gan using cpu.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HLdcmMr4aBRv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_images(images):\n",
        "  h=args.screen_size\n",
        "  fig=plt.figure(figsize=(16, 16))\n",
        "  columns = len(images)\n",
        "  rows = 1\n",
        "\n",
        "  for i, img in enumerate(images):\n",
        "    img = img[:, :, :3]\n",
        "    #print(img.shape)\n",
        "    fig.add_subplot(rows, columns, i+1)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(img)\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UnK-c4K4ci15",
        "colab_type": "code",
        "outputId": "5da21f78-10c0-4a23-b3d9-7b06f9071328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def experiment(to_plot, fn, actual=None):\n",
        "  test_gan.load_checkpoint(fn, actual)\n",
        "  images_to_plot = images[to_plot:to_plot+10].astype(np.float32)/255.\n",
        "  plot_images(images_to_plot)\n",
        "  decoded = test_gan.sess.run(test_gan.y, feed_dict={test_gan.actions: actions[to_plot:to_plot+10]})\n",
        "  plot_images(decoded)\n",
        "  plt.show()\n",
        "  \n",
        "experiment(10, 'tf_gan3')\n",
        "#experiment(80, 'tf_vae', 'tf_vae/vae-189999')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('loading model', u'tf_gan3/gan-58443')\n",
            "INFO:tensorflow:Loading model tf_gan3/gan-58443.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "INFO:tensorflow:Restoring parameters from tf_gan3/gan-58443\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ej6NYkjKGbDSkXhaMr++2qrm2LDl47w5XX38NIdIovpZEIrn10i3C7Viyosxp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\nKIqiKIrSK2qIKoqiKIqiKIqiKL3yf5DRx98Ry2eRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hKJuqbro0XaUJNb7qMickQOMgMYQmoFm33pgAmJd4Gl99yd9sHWsP9WYHkXOf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\nYII=\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yQGTRRcABVw9",
        "colab_type": "code",
        "outputId": "550ec215-0b5c-4430-8fda-ed65bb12d1a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def experiment5(test_models, duration=10.0):\n",
        "  env = ColorEnv(args, paint_mode=PaintMode.STROKES_ONLY)\n",
        "  env.reset()\n",
        "  NUM_ACS = 8\n",
        "  acs = [env.random_action() for _ in range(NUM_ACS)]\n",
        "  \n",
        "  def frame(t):\n",
        "    t_ = t / duration\n",
        "    t = np.abs((1.0-np.cos(NUM_ACS*np.pi*np.mod(t_, 1./NUM_ACS)))/2.0)\n",
        "    \n",
        "    new_ac = (1-t)*acs[int(np.floor(t_*NUM_ACS))] + t*acs[int((np.floor(t_*NUM_ACS)+1)%NUM_ACS)]\n",
        "    env.draw(new_ac)\n",
        "    im = env.image\n",
        "    im = im[:, :, :3]\n",
        "    stack_these = []\n",
        "    for test_gan in test_models:\n",
        "      decoded = test_gan.sess.run(test_gan.y, feed_dict={test_gan.actions: [new_ac]})[0]\n",
        "      decoded = (decoded*255).astype(np.uint8)\n",
        "      concatted = np.concatenate([im, decoded], 1)\n",
        "      stack_these.append(concatted)\n",
        "    return np.concatenate(stack_these, axis=0)\n",
        "  \n",
        "  clip = mpy.VideoClip(frame, duration=duration)\n",
        "  clip.write_videofile('tmp.mp4', fps=30.0)\n",
        "  display(mpy.ipython_display('tmp.mp4', height=400))\n",
        "\n",
        "  \n",
        "  \n",
        "_test_gans = [ConvGAN(learning_rate=learning_rate,\n",
        "              is_training=False,\n",
        "              reuse=False,\n",
        "              gpu_mode=False,\n",
        "              divisor=4,\n",
        "              add_noise=False) for x in range(1)]\n",
        "_test_gans[0].load_checkpoint('tf_gan3')\n",
        "#_test_gans[1].load_checkpoint('tf_gan3', 'tf_gan3/gan-400000')\n",
        "#_test_gans[2].load_checkpoint('tf_gan3', 'tf_gan3/gan-300000')\n",
        "experiment5(_test_gans, 20.)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:conv_gan using cpu.\n",
            "('loading model', u'tf_gan3/gan-58443')\n",
            "INFO:tensorflow:Loading model tf_gan3/gan-58443.\n",
            "INFO:tensorflow:Restoring parameters from tf_gan3/gan-58443\n",
            "[MoviePy] >>>> Building video tmp.mp4\n",
            "[MoviePy] Writing video tmp.mp4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|█████████▉| 600/601 [00:08<00:00, 73.24it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: tmp.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<moviepy.video.io.html_tools.HTML2 object>"
            ],
            "text/html": [
              "<div align=middle><video 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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "NApfpglwT9V0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}